Motion blur may degrade image quality, in particular for X-ray mammography where images have to be very sharp to see small micro-calcifications.
In a typical workflow of screening mammography, patients leave the clinic before the images are thoroughly reviewed. When the motion blur is discovered, patients may have to be called back to acquire new images, which may cause inconvenience, anxiety and additional expenses. Normally, the “motion blur” origins from the small displacements of the patient's movements.
Efficient workflow is very important for specialized screening mammography clinics where healthy patients are examined on a regular basis. Speed requirements and cost control have driven many clinics to introduce a workflow that resembles an assembly line. Thus, the demands and requirements for mammography systems are different compared to other applications of medical X-ray imaging.
FIG. 1 illustrates a known configuration for an X-ray imaging system that scans a breast and acquires a set of overlapping part images, and computes a resultant image from said part images. The part images arise from irradiation of a human breast using a bundle of X-ray beams. Said bundle of beams is created by an X-ray source 110 and a collimator 120 with a set of long narrow apertures, also referred to a slits. Each slit forms a thin X-ray beam, which irradiates the imaged breast and rays passing through are received by a photon counting line detector. There is one line detector for each slit, mounted in a common detector unit 150.
In Ph.D. thesis “Motion Estimation and Compensation in Medical Imaging” by Magnus Hemmendorff, Linköping Studies in Science and Technology, dissertations thesis No. 703, Jul. 2001, hereby expressly incorporated by reference in its entirety, methods for generation of local motion constraints are described. One method is based on phase from quadrature filters; another is based on canonical correlation and scalar products of quadrature filters. In both methods, a local confidence measure is produced to increase accuracy and robustness. The local phase variations are used to generate local motion constraints and a motion field can be estimated by least square fit of parametric motion models, such as shift models, affine models and finite element models.